unnatipatil2005/ALgerian-Forest-Fire-Dataset-Regression

🌲 Algerian Forest Fire Dataset — Regression Project 📌 Overview This project predicts the Fire Weather Index (FWI) using multiple environmental features such as temperature, humidity, wind speed, and more. It uses Ridge Regression for predictive modeling and a Flask web app for interactive user input and result visualization. 🧠 Problem Statement Wildfires can cause massive environmental and economic damage. The goal of this project is to predict the FWI — a metric representing the potential for forest fire — based on meteorological data from the Algerian Forest Fire dataset. ⚙️ Tech Stack Python 3 Flask (for web framework) Scikit-learn (for regression and scaling) HTML/CSS (for frontend interface) Pickle (for model serialization) NumPy & Pandas (for data processing) 🧩 Project Structure 📁 Algerian-Forest-Fire-Dataset-Regression │ ├── app.py # Flask application ├── models/ │ ├── ridge.pkl # Trained Ridge Regression model │ └── scaler.pkl # StandardScaler object │ ├── templates/ │ ├── home.html # Web form for user input │ └── index.html # Landing page │ ├── static/ # (optional) CSS/JS files │ ├── README.md # Project documentation └── requirements.txt # Python dependencies 🚀 How to Run Locally 1️⃣ Clone the repository git clone github.com cd ALgerian-Forest-Fire-Dataset-Regression 2️⃣ Create and activate virtual environment python -m venv venv venv\Scripts\activate # (Windows) # or source venv/bin/activate # (Mac/Linux) 3️⃣ Install dependencies pip install -r requirements.txt 4️⃣ Run the Flask app python app.py Then open your browser and go to: 👉 127.0.0.1 🧪 Model Details Algorithm: Ridge Regression Evaluation Metrics: R² Score, MAE, MSE Preprocessing: Standard Scaling applied to all numeric features 🖼️ Web App Interface The app provides a sim …